Performance Analysis and Resource Allocations for a WPCN With a New Nonlinear Energy Harvester Model
Bibliographic record
Abstract
Wireless powered communication networks (WPCNs) are commonly analyzed by using the linear energy harvesting (EH) model. However, since practical EH circuits are non-linear, the use of the linear EH model gives rise to distortions and mismatches. To overcome these issues, we propose a more realistic, nonlinear EH model. The model is based upon the error function and has three parameters. Their values are determined to best fit with measured data. We also develop the asymptotic version of this model. For comparative evaluations, we consider the linear and rational EH models. With these four EH models, we investigate the performance of a WPCN. It contains a multiple-antenna power station (PS), a signal-antenna wireless device (WD), and a multiple-antenna information receiving station (IRS). The WD harvests the energy broadcast by the PS in the PS-WD link, and then it uses the energy in the WD-IRS link to transfer information. We analyze the average throughput of delay-limited and delay-tolerant transmission modes as well as the average bit error rate (BER) of binary phase-shift keying (BPSK) and binary differential phase-shift keying (BDPSK) over the four EH modes. As well, we derive the asymptotic expressions for the large PS antenna case and the effects of transmit power control. Furthermore, for the case of multiple WDs, we optimize energy beamforming and time allocation to maximize the minimum rate of the WDs. Finally, the performances of four EH models are validated by Monte-Carlo simulations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".